اقتصاد و تجارت کردن تولید گازهای گلخانه ای CO2: یک روش سیستماتیک برای بهینه سازی سرمایه گذاری در اقدامات یکپارچه سازی فرآیند تحت عدم قطعیت

In this paper we present a systematic approach for taking into account the resulting CO2 emissions reductions from investments in process integration measures in industry when optimizing those investments under economic uncertainty. The fact that many of the uncertainties affecting investment decisions are related to future CO2 emissions targets and policies implies that a method for optimizing not only economic criteria, but also greenhouse gas reductions, will provide better information to base the decisions on, and possibly also result in a more robust solution. In the proposed approach we apply a model for optimization of decisions on energy efficiency investments under uncertainty and regard the decision problem as a multiobjective programming problem. The method is applied to a case of energy efficiency investments at a chemical pulp mill. The case study is used to illustrate that the proposed method provides a good framework for decision-making about energy efficiency measures when considerations regarding greenhouse gas reductions influence the decisions. We show that by setting up the problem as a multiobjective programming model and at the same time incorporating uncertainties, the trade-off between economic and environmental criteria is clearly illustrated.

Investment decisions in industry are often based on a number of conflicting objectives, although economy is usually the main focus. The increased climate concern in society makes, however, the CO2 emissions associated with industrial investments a more important issue. For strategic investments especially, economy and emissions reductions depend on the future energy market. Electricity and fuel prices, marginal electricity production and marginal wood fuel usage, and emissions charges and taxes are all examples of energy market parameters that are highly uncertain, but directly influence the profitability and the CO2-reducing potential of the investments. The aim of this paper is to present a systematic approach for analysis of the trade-off between economy and CO2 emissions when investments are optimized under uncertainty. A methodology for identification of robust investments in energy efficiency under uncertainty [1], [2] and [3] is here further developed to include multiple objectives and is then applied in a case study. The purpose is to illustrate how the previously published single-objective model can be extended to include both an economic and an environmental objective. Many uncertainties affecting investment decisions are related to future CO2 emissions targets and policies, which implies that a method for optimization of both economic and environmental criteria will provide better information for decision-makers in industry to base the decisions on. Most strategies for improvement of the energy efficiency of an industrial plant will lead to reductions of CO2 emissions if a wide systems perspective is employed. By reducing the use of fossil fuels, emissions are decreased on-site. Biomass is generally assumed to be CO2-neutral; nevertheless, the reduction of wood fuel use will also lead to CO2 emissions reductions, but in this case off-site, since reduced usage enables substitution of fossil fuels elsewhere. Also decreased imports or increased exports of electricity will affect the net CO2 emissions. The pulp and paper industry, from which the case study of this project is taken, is the fourth largest industrial energy user in the world [4], which makes it important in the progress to mitigate climate change. Cost-effective energy savings and potential CO2 reductions have been identified in the pulp and paper sector in several studies [5], [6] and [7]. The cost of CO2 reduction is, however, dependent on, for example, the electricity prices and the marginal electricity production, which are uncertain parameters. Furthermore, the trade-off between cost-effectiveness and CO2 reductions is unclear. By applying the methodology proposed by Svensson et al. [1], the uncertainties are directly incorporated in the optimization, and the trade-off between CO2 reductions and profitability can easily be analyzed.

In this paper, we present a multiobjective approach for the optimization of investments in energy efficiency under energy market uncertainty. The proposed approach is based on a previously presented methodology for optimizing such investments under uncertainty with respect only to an economic objective [1]. We show that the multiobjective approach will increase the knowledge of the trade-off between economic and environmental considerations in the decision-making regarding such investments. Uncertainties can be incorporated in the optimization model also in the multiobjective model formulation. The multiobjective approach enables the use of Pareto graphs for illustrating the trade-off between the economic and the CO2 objective. A Pareto graph clearly illustrates the relationship between the two criteria. We also propose the use of target graphs, where the CO2 emissions for one solution are plotted, for each scenario, together with the best possible emissions reductions for that scenario. This kind of graph will provide an aid in the decision-making process, since due to differing marginal electricity production and wood fuel use, the CO2 emissions reductions will vary between the scenarios even when the same energy efficiency measures are taken. For the case study presented here, the target graph shows that the CO2 emissions reductions corresponding to an economically optimal solution for reasonable probability distributions is quite close to what is maximally achievable. This indicates a robustness of this economic optimum solution, confirming the results of previous work [2] and [3]. Finally, the investments characterizing the Pareto-optimal solutions can be illustrated in graphs showing the initial investment as a function of CO2 emissions reductions. This kind of graph will provide basic information regarding the investments to roughly explain the characteristics of the Pareto graph. Details about the investment plans can then be achieved through a closer look at the solution data for the interesting solutions.